Principal Data Architect
Hybrid · St. Petersburg, Florida, United States
Job Summary
Lead enterprise data architecture initiatives including defining reference architectures for data Lakehouse and warehouse platforms, designing logical/conceptual/physical data models, establishing standards and reusable patterns, defining data access and consumption strategies, guiding platform and technology choices, and shaping real-time and batch data architectures. Collaborate with senior technology leaders, data management, security, and business stakeholders to deliver scalable, secure data capabilities; drive governance, metadata, lineage, and data quality; demonstrate thought leadership in cloud data platforms, data mesh, and AI-ready data. Role requires hybrid schedule with 2-3 in-office days per week at the St. Petersburg, FL corporate office; extensive experience in wealth/asset management or related financial services domains; AWS data ecosystem expertise; strong communication and leadership skills.
Required Qualifications
- Bachelor's degree in Computer Science or related field
- 10+ years of progressive experience in data architecture, data engineering, database architecture, enterprise architecture, or large-scale data platform delivery
- Proven ability to influence senior stakeholders and lead through ambiguity
- Expert level knowledge of Data Architecture, Data Modeling, and data lakehouse/data warehouse design methodologies (star schema, snowflake, normalization/denormalization)
- Proficiency with Oracle (RAC, Exadata), SQL Server, AWS Redshift; experience with replication tools like Oracle GoldenGate and AWS DMS
- Advanced SQL, PL/SQL development, and database performance tuning
- Deep expertise in AWS Data Ecosystem (Athena, Iceberg, Lake Formation, Glue, EMR, Sagemaker, S3, Airflow, Aurora, Presto)
- Scripting and automation (Shell, Python)
- Data integration architecture (ETL/ELT, streaming, event-driven, API-based, file-based, replication-based) with data contracts, schema evolution, lineage, quality checks, and monitoring
- Data Lakehouse & Data Marketplace experience; governance, metadata, lineage, and data quality embedding into platforms
- AI data readiness and semantic data enablement for AI/GenAI use cases
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